论文标题
使用生成对抗网络对胸部X射线图像中的肺部进行分割
Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks
论文作者
论文摘要
胸部X射线（CXR）是一种低成本的医学成像技术。与MRI，CT和PET扫描相比，这是鉴定许多呼吸道疾病的常见程序。本文介绍了使用生成对抗网络（GAN）在给定CXR上执行肺部分割的任务。通过学习从一个域到另一个域的映射来生成逼真的数据，GAN很受欢迎。在我们的工作中，对GAN的生成器进行了训练，以生成给定输入CXR的分段面膜。歧视者区分地面真理和生成的面具，并通过对抗性损失度量更新发电机。目的是为输入CXR生成面具，与地面真相面具相比，它们尽可能现实。该模型分别使用称为D1，D2，D3和D4的四个不同的歧视器对模型进行训练和评估。三个不同CXR数据集的实验结果表明，所提出的模型能够达到0.9740的骰子得分，而IOU得分为0.943，比其他报告的最先进的结果要好。
Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3, and D4, respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results.
